基于局部熵的主动轮廓模型

针对传统C-V模型对灰度分布不均匀图像分割效果不理想的问题,研究了一种基于局部熵的主动轮廓模型.首先,算法将局部熵的概念引入到C-V模型中,通过核函数获得局部区域的不均匀信息,来构建局部熵能量函数；其次,采用变分水平集的方法,最小化局部熵能量泛函,得到水平集的梯度下降流,根据梯度下降流不断更新水平集,获得目标轮廓图；最后,对4组灰度严重不均匀的图像进行仿真实验,并将本文算法与LBF方法和LGDF方法进行对比.实验结果表明,与LBF方法和LGDF方法相比,本文算法实现了灰度不均匀的图像的精确分割.

Active contour model driven by local entropy energy

Using the C-V model to segment images with intensity inhomogeneity, the segmentation results are often not very good. Therefore, we propose an active contour model based on the local entropy energy. First, we introduce the concept of local entropy into the C-V model to get inhomogeneity information in local regions according to the kernel function and to model the local entropy energy function. Second, we use a variable level set to minimize the local entropy function and to get the gradient descent flow of the level set. Finally, simulation experiments are carried out on four severe intensity inhomogeneity images, and the results are compared to the proposed method with LBF and LGDF methods. It is shown that our method achieves more accurate segmentation results for intensity inhomogeneity images compared to the LBF and LGDF methods.